| 基于改进SICP算法的多源点云配准研究 |
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| 引用本文： | 梁四幺,周光耀,李茂森.基于改进SICP算法的多源点云配准研究[J].海洋测绘,2021(4):53-56. |
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| 作者姓名： | 梁四幺 周光耀 李茂森 |
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| 作者单位： | 核工业航测遥感中心,河北 石家庄 050000;东南数字经济发展研究院,浙江 衢州 324000 |
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| 摘 要： | 三维激光点云数据具有精度高、数据获取高效、几何信息丰富的优势,在地形数据获取方面起到了越来越重要的作用。但在实际的外业测量中,由于视场角限制,一般都难以获取待测物体完整的点云数据,发生数据缺失现象。而根据摄影测量技术生成密集的影像点云,能获取复杂区域的测量数据。针对三维激光点云数据外业采集缺失的状况,结合影像密集点云特征,提出了一种加入动态迭代因子和分步最优求解尺度的改进尺度迭代最近点(scaling iterative closest point, SICP)算法,对影像点云与三维激光点云进行配准研究。实验结果表明:基于改进的SICP算法提高了影像点云与三维激光点云的配准精度、减少了迭代次数,能有效解决不同源平台获取的点云数据融合问题。
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| 关 键 词： | 摄影测量 影像点云 三维激光点云 点云配准 孔洞修补 改进SICP算法 |
Research on multi-source point cloud registration based on improved SICP algorithm |
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| LIANG Siyao,ZHOU Guangyao,LI Maosen.Research on multi-source point cloud registration based on improved SICP algorithm[J].Hydrographic Surveying and Charting,2021(4):53-56. |
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| Authors: | LIANG Siyao ZHOU Guangyao LI Maosen |
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| Institution: | Airborne Survey and Remote Sensing Center of Nuclear Industry,Shijiazhuang 050000 ,China;Southeast Digital Economy Development Research Institute,Quzhou 324000 ,China |
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| Abstract: | Laser point cloud data has the advantages of high accuracy,efficient data acquisition,and rich geometric information,and has played an increasingly important role in the acquisition of terrain data.However,in actual field surveys,due to the limitation of the field of view,it is generally difficult to obtain the complete point cloud data of the object to be measured,and the phenomenon of data loss occurs.According to the photogrammetry technology,the dense image point cloud is generated,which can obtain the measurement data of the complex area.Aiming at the lack of field collection of 3D laser point cloud data and combining the features of dense point clouds of images,this paper proposes an improved scaling iterative closest point(SICP)that adds dynamic iterative factors and stepwise optimal solution scales.The algorithm performs registration research on the image point cloud and the 3D laser point cloud.The experimental results show that the improved SICP algorithm improves the registration accuracy of the image point cloud and the 3D laser point cloud,reduces the number of iterations,and can effectively solve the problem of point cloud data fusion obtained from different source platforms. |
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